{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Neural Networks with PyTorch\n",
        "\n",
        "In this notebook we are going to implement and train a neural network with PyTorch!\n",
        "\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SharifiZarchi/Introduction_to_Machine_Learning/blob/main/Jupyter_Notebooks/Chapter_03_Neural_Networks/NNs_with_torch.ipynb)\n",
        "[![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/SharifiZarchi/Introduction_to_Machine_Learning/main/Jupyter_Notebooks/Chapter_03_Neural_Networks/NNs_with_torch.ipynb)"
      ],
      "metadata": {
        "id": "Ecaqr9YkWpbL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports and Setup"
      ],
      "metadata": {
        "id": "4XApd8re1Vt7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We start by importing necessary modules. Don't worry if it seems a little overwhelming at first."
      ],
      "metadata": {
        "id": "aDJTtiVD1dfh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor"
      ],
      "metadata": {
        "id": "-gbTJDvZ1X0X"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title plotting functions\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from tqdm import trange\n",
        "\n",
        "def visualize(images, labels):\n",
        "    \"\"\"\n",
        "    Visualize a batch of images.\n",
        "    \"\"\"\n",
        "    fig, axes = plt.subplots(8, 8, figsize=(10, 10))\n",
        "    for i, ax in enumerate(axes.flat):\n",
        "        ax.imshow(images[i].squeeze(), cmap='gray')\n",
        "        ax.set_title(f'Label: {labels[i].item()}')\n",
        "        ax.axis('off')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def visualize_predictions(images, labels, predicted_labels):\n",
        "    \"\"\"\n",
        "    Visualize a batch of images with their true and predicted labels.\n",
        "    Titles are green if the prediction is correct, red if incorrect.\n",
        "    \"\"\"\n",
        "    fig, axes = plt.subplots(8, 8, figsize=(11, 12))\n",
        "    for i, ax in enumerate(axes.flat):\n",
        "        ax.imshow(images[i].squeeze(), cmap='gray')\n",
        "        color = 'green' if labels[i].item() == predicted_labels[i].item() else 'red'\n",
        "        ax.set_title(f'True: {labels[i].item()}\\nPred: {predicted_labels[i].item()}', color=color)\n",
        "        ax.axis('off')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def plot_conf_mat(model, dataloader, device):\n",
        "    \"\"\"\n",
        "    Plot the confusion matrix for a given model and dataloader.\n",
        "    \"\"\"\n",
        "    # Initialize the confusion matrix\n",
        "    total, correct = 0, 0\n",
        "    conf_mat = torch.zeros((10, 10))\n",
        "    with torch.no_grad():\n",
        "        for x, y in dataloader:\n",
        "            x, y = x.to(device), y.to(device)\n",
        "            pred = model(x)\n",
        "            total += pred.shape[0]\n",
        "            pred = torch.argmax(pred, axis=1)\n",
        "            correct += sum(pred == y).item()\n",
        "            for j in range(pred.shape[0]):\n",
        "                conf_mat[y[j], pred[j].item()] += 1\n",
        "    # calculate the normalized confusion matrix\n",
        "    norm_conf_mat = conf_mat / torch.sum(conf_mat, axis=1)\n",
        "    # plot the matrix\n",
        "    fig, ax = plt.subplots()\n",
        "    plt.imshow(norm_conf_mat)\n",
        "    plt.title('Confusion Matrix')\n",
        "    plt.xlabel('Predictions')\n",
        "    plt.ylabel('Labels')\n",
        "    plt.xticks(range(10))\n",
        "    plt.yticks(range(10))\n",
        "    plt.colorbar()\n",
        "    # put number of each cell in plot\n",
        "    for i in range(10):\n",
        "        for j in range(10):\n",
        "            c = conf_mat[j, i]\n",
        "            color = 'black' if c > 500 else 'white'\n",
        "            ax.text(i, j, str(int(c)), va='center', ha='center', color=color)\n",
        "    plt.show()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "djIbr1Vn-CH3"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset"
      ],
      "metadata": {
        "id": "b9_crXc91ull"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use `datasets` from `torchvision` to load the [MNIST](https://yann.lecun.com/exdb/mnist/) handwritten digits dataset. You can find the list of datasets available on torchvision [here](https://pytorch.org/vision/0.8/datasets.html). Now let's take a loot at the parameters we set:\n",
        "\n",
        "\n",
        "\n",
        "*   `root` sets the directory we store and load our data from.\n",
        "*   `train` indicates wether we want the training dataset or the test dataset.\n",
        "*   `transform` allows us to apply transformations to our data, here we are only going to convert the data to tensor so that they work with PyToch, however in the future notebooks you will see more complicated transformations.\n",
        "\n"
      ],
      "metadata": {
        "id": "WYDA-qWO4EYc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download training data from open datasets.\n",
        "training_data = datasets.MNIST(\n",
        "    root=\"data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        ")\n",
        "\n",
        "# Download test data from open datasets.\n",
        "test_data = datasets.MNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        ")\n",
        "\n",
        "\n",
        "print(f\"Training data: {training_data}\\n\")\n",
        "print(f\"Test data: {test_data}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OxFSah-P1v6o",
        "outputId": "165405d5-6aba-4361-8e8e-6305f12fe12c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 11809117.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 360875.18it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 3281533.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 4281017.70it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Training data: Dataset MNIST\n",
            "    Number of datapoints: 60000\n",
            "    Root location: data\n",
            "    Split: Train\n",
            "    StandardTransform\n",
            "Transform: ToTensor()\n",
            "\n",
            "Test data: Dataset MNIST\n",
            "    Number of datapoints: 10000\n",
            "    Root location: data\n",
            "    Split: Test\n",
            "    StandardTransform\n",
            "Transform: ToTensor()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see there are 60000 training samples in the training dataset and there are 10000 samples in the test dataset."
      ],
      "metadata": {
        "id": "-IefrZMW7NQR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Loaders"
      ],
      "metadata": {
        "id": "7YCOmNEF7hPy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To make loading and working with the data easier, we are going to use `DataLoader` from `torch.utils.data`. The `DataLoader` takes in a dataset and a `batch_size` parameter, and allows us to iterate over the dataset. Here we do one iteration just to see the data shapes:"
      ],
      "metadata": {
        "id": "mRqzyZ2j7j2l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 64\n",
        "\n",
        "# Create data loaders.\n",
        "train_dataloader = DataLoader(training_data, batch_size=batch_size)\n",
        "test_dataloader = DataLoader(test_data, batch_size=batch_size)\n",
        "\n",
        "# Iterate over the data\n",
        "for x, y in test_dataloader:\n",
        "    print(f\"Shape of X [N, C, H, W]: {x.shape}\")\n",
        "    print(f\"Shape of y: {y.shape} {y.dtype}\")\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnLKuT5b3RNe",
        "outputId": "66d69918-d404-407f-f220-236f47c82f99"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])\n",
            "Shape of y: torch.Size([64]) torch.int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see the `x`s are of shape `[64, 1, 28, 28]` which means we have a batch of `64` images, each with `1` channel which means the images are grayscale (for example colorful images have 3 channels of red, blue and green or RGB), and of size `28x28` pixels.\n",
        "\n",
        "Similarly the `y`s are of shape `[64]` which means we have a batch of 64 labels. In the next section we will learn more about these labels."
      ],
      "metadata": {
        "id": "l5kFlIJa8fMz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualization"
      ],
      "metadata": {
        "id": "Xe4pHWs691py"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we will take a look at single batch of data and visualize it."
      ],
      "metadata": {
        "id": "U3eIyvqY93JC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "images, labels = next(iter(train_dataloader))\n",
        "\n",
        "visualize(images, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cfN57uEp9-Jv",
        "outputId": "b0bf505c-8929-485e-b7fe-8367eb319eb2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 64 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Device"
      ],
      "metadata": {
        "id": "LtFDQWM4ASCS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To accelerate operations in the neural network, we move it to the GPU or MPS (for Apple silicon) if available."
      ],
      "metadata": {
        "id": "4E-odHSoAlIj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get cpu, gpu or mps device for training.\n",
        "device = (\n",
        "    \"cuda\"\n",
        "    if torch.cuda.is_available()\n",
        "    else \"mps\"\n",
        "    if torch.backends.mps.is_available()\n",
        "    else \"cpu\"\n",
        ")\n",
        "print(f\"Using {device} device\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBYNiZZFAT9m",
        "outputId": "a30d8641-f2e8-40f7-af01-1e1cd40efef0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using cuda device\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model"
      ],
      "metadata": {
        "id": "mQyyFmmBAvcH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we define our model. Recall that each batch of image has a shape of `[64, 1, 28, 28]`. For now we only want to use `Linear` layers so we must **flatten** the inputs so that we can pass it to the linear layers. The `nn.Flatten()` module allows us to do this."
      ],
      "metadata": {
        "id": "ioQyu5ChA4zA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class NeuralNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.flatten = nn.Flatten()\n",
        "        self.linear_relu_stack = nn.Sequential(\n",
        "            nn.Linear(28*28, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 10)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.flatten(x)\n",
        "        logits = self.linear_relu_stack(x)\n",
        "        return logits"
      ],
      "metadata": {
        "id": "WEDxIof6Awyn"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next we initialize our model."
      ],
      "metadata": {
        "id": "LK_HFak6ChOT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = NeuralNetwork().to(device)\n",
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqMh9dC-CkqO",
        "outputId": "46bf1afd-3150-434b-e172-0ec581a43dee"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NeuralNetwork(\n",
            "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
            "  (linear_relu_stack): Sequential(\n",
            "    (0): Linear(in_features=784, out_features=512, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=512, out_features=512, bias=True)\n",
            "    (3): ReLU()\n",
            "    (4): Linear(in_features=512, out_features=10, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loss Function"
      ],
      "metadata": {
        "id": "NZL-dYlMCtKh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since we are trying to classify the handwritten digits, we are going to use the cross entropy loss. You can see the list of loss functions in PyTorch [here](https://pytorch.org/docs/stable/nn.html#loss-functions)."
      ],
      "metadata": {
        "id": "uoUk5yhZCvQI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "dJltpy3MCu4w"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Optimizer"
      ],
      "metadata": {
        "id": "IfKNbTfcDL72"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next we need to setup an optimizer for training our model. We use stochastic gradient descent so we have must use the `SGD` module from `torch.optim`. We must pass the `model.parameters()` to the `SGD` optimizer and set its learning rate `lr=1e-3`. In the following sessions you will learn more about different optimizers but can also learn about the optimizers available on PyTorch [here](https://pytorch.org/docs/stable/optim.html)."
      ],
      "metadata": {
        "id": "q8HTPwT8DRQc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)"
      ],
      "metadata": {
        "id": "ZmjqBHF8DOfK"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "uryY-hUQDNw2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To train the model we first set the number of epochs. For each epoch we then iterate over the entire training data and update the model parameters. For each batch of data, we must first move the data to same device as the network, then we predict the output of the model, calculate the loss, perform backward pass, update parameters, and reset the gradients.\n",
        "\n",
        "To monitor training, we use `trange` from `tqdm` which performs similar to `range` but allows us to have a progress bar `pbar` which lets us display useful information."
      ],
      "metadata": {
        "id": "FrvYbCNcJKzw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of epochs we wish to train the model\n",
        "n_epochs = 30\n",
        "\n",
        "for _ in (pbar := trange(n_epochs)):\n",
        "    # Iterate over the data\n",
        "    for x, y in train_dataloader:\n",
        "        # Move the datapoints to same device as the model\n",
        "        x, y = x.to(device), y.to(device)\n",
        "        # Predict the output and perform the forward pass\n",
        "        pred = model(x)\n",
        "        # Compute prediction error\n",
        "        loss = loss_fn(pred, y)\n",
        "        # Backpropagation\n",
        "        loss.backward()\n",
        "        # Update the model weights\n",
        "        optimizer.step()\n",
        "        # Clear the gradients\n",
        "        optimizer.zero_grad()\n",
        "        # Update the progress bar\n",
        "        pbar.set_description(f'Loss = {loss.item():.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhAslicTEFwi",
        "outputId": "78fe1044-26f7-4f25-bf05-ab24ce9d60f5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss: 0.175: 100%|██████████| 30/30 [06:40<00:00, 13.36s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the following notebooks we explore more advanced methods for monitoring the training but what you"
      ],
      "metadata": {
        "id": "cjCxEVVFLVj0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "qkaReM2DL-DN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally we can evaluate the trained model. We will start by evaluating the model on the test dataset. Here we use `torch.no_grad()` since we don't need the gradients. We iterate over the entire test dataset and print the accuracy of our model on this dataset."
      ],
      "metadata": {
        "id": "K81CBFV6RPwH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Store the number of correctly classified and total labels\n",
        "correct, total = 0, 0\n",
        "\n",
        "# Disable gradient calculation\n",
        "with torch.no_grad():\n",
        "    # Iterate over the test data\n",
        "    for x, y in test_dataloader:\n",
        "        # Move the datapoints to same device as the model\n",
        "        x, y = x.to(device), y.to(device)\n",
        "        # Predict the output\n",
        "        logits = model(x)\n",
        "        # Get the predicted label\n",
        "        pred = torch.argmax(logits, axis=1)\n",
        "        # Update the number of correclty classified labels\n",
        "        correct += sum(pred == y).item()\n",
        "        # Update the number of total labels\n",
        "        total += pred.shape[0]\n",
        "\n",
        "print(f'Accuracy: {100 * correct / total:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dH2Ljj-IG-JJ",
        "outputId": "46ab4ca3-f3cc-4a7b-f017-e8d8caa94658"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 90.64%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's visuzlie a batch to compare the predictions and the true labels."
      ],
      "metadata": {
        "id": "cDwSUz1lR4In"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "images, labels = next(iter(test_dataloader))\n",
        "preds = torch.argmax(model(images.to(device)), axis=1).cpu()\n",
        "\n",
        "visualize_predictions(images, labels, preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "M11iJLo0OBfQ",
        "outputId": "05923ef6-db3d-4833-d697-a1c371c0225b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x1200 with 64 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To get a better sense of our model, we can plot it's confusion matrix."
      ],
      "metadata": {
        "id": "cTqq3z4RSDHp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_conf_mat(model, test_dataloader, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "cpffebCFNXh3",
        "outputId": "da3ea4db-2833-4586-e4c1-683662706d80"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Refrences\n",
        "\n",
        "\n",
        "*   [Learn the Basics of PyTorch](https://pytorch.org/tutorials/beginner/basics/intro.html)\n",
        "*   [Neural networks](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) from [3Blue1Brown](https://www.3blue1brown.com/)\n",
        "\n"
      ],
      "metadata": {
        "id": "ww709YMt17Mg"
      }
    }
  ]
}
